Publications
- 2021
- Published
Learning Heuristic Selection with Dynamic Algorithm Configuration
Speck, D., Biedenkapp, A., Hutter, F., Mattmüller, R. & Lindauer, M., 5 Dec 2021, Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS). Biundo, S., Do, M., Goldman, R., Katz, M., Yang, Q. & Zhuo, H. H. (eds.). p. 597-605 9 p. (Proceedings International Conference on Automated Planning and Scheduling, ICAPS; vol. 2021-August).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
Explaining Hyperparameter Optimization via Partial Dependence Plots
Moosbauer, J., Herbinger, J., Casalicchio, G., Lindauer, M. & Bischl, B., 8 Nov 2021, (E-pub ahead of print) Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) . 21 p.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F. & Lindauer, M., 5 Oct 2021, (E-pub ahead of print) Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021. 20 p.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Published
Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
Zimmer, L., Lindauer, M. & Hutter, F., 1 Sept 2021, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 43, 9, p. 3079-3090 12 p., 9382913.Research output: Contribution to journal › Article › Research › peer review
- Published
Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019
Liu, Z., Pavao, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Junior, J. C. S. J., Li, G., Lindauer, M., Luo, Z., Madadi, M., Nierhoff, T., Niu, K., Pan, C., Stoll, D., Treguer, S., Wang, J., Wang, P., Wu, C., Xiong, Y., Zela, A. & Zhang, Y., 1 Sept 2021, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 43, 9, p. 3108-3125 18 p., 9415128.Research output: Contribution to journal › Article › Research › peer review
- Published
Self-Paced Context Evaluation for Contextual Reinforcement Learning
Eimer, T., Biedenkapp, A., Hutter, F. & Lindauer, M., 18 Jul 2021, Proceedings of the international conference on machine learning (ICML). ML Research Press, 14 p. (Proceedings of Machine Learning Research).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
Automatic Risk Adaptation in Distributional Reinforcement Learning
Schubert, F., Eimer, T., Rosenhahn, B. & Lindauer, M., 11 Jun 2021, (E-pub ahead of print) 14 p.Research output: Working paper/Preprint › Preprint
- Published
Verfahren, Vorrichtung und Computerprogramm zum Erstellen eines künstlichen neuronalen Netzes
Lindauer, M. (Inventor), Hutter, F. (Inventor), Burkart, M. (Inventor) & Zimmer, L. (Inventor), 25 Mar 2021, IPC No. G06N 3/ 08 A I, Patent No. DE102019214625, Priority date 25 Sept 2019, Priority No. DE201910214625Research output: Patent
- Published
METHOD, DEVICE AND COMPUTER PROGRAM FOR PRODUCING A STRATEGY FOR A ROBOT
Hutter, F. (Inventor), Fuks, L. (Inventor), Lindauer, M. (Inventor) & Awad, N. (Inventor), 14 Jan 2021, IPC No. G05B 17/ 02 A I, Patent No. US2021008718, Priority date 12 Jul 2019, Priority No. DE201910210372Research output: Patent
- Published
Verfahren, Vorrichtung und Computerprogramm zum Erstellen einer Strategie für einen Roboter
Hutter, F., Fuks, L., Lindauer, M. & Awad, N., 14 Jan 2021, IPC No. G05B13/02, G06N20/00, G06N3/02, Patent No. DE102019210372A1, 7 Dec 2019, Priority date 7 Dec 2019, Priority No. DE102019210372AResearch output: Patent
- E-pub ahead of print
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T., Deng, D., Lindauer, M. & Hutter, F., 2021, (E-pub ahead of print) ICML 2021 Workshop AutoML. 22 p.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Published
Bayesian Optimization with a Prior for the Optimum
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M. & Hutter, F., 2021, Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Proceedings. Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J. & Lozano, J. A. (eds.). Cham: Springer Nature Switzerland AG, Vol. 3. p. 265-296 32 p. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); vol. 12977).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Published
DACBench: A Benchmark Library for Dynamic Algorithm Configuration
Eimer, T., Biedenkapp, A., Reimer, M., Adriaensen, S., Hutter, F. & Lindauer, M. T., 2021, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21). Zhou, Z.-H. (ed.). p. 1668-1674 7 p. (IJCAI International Joint Conference on Artificial Intelligence).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
Eggensperger, K., Müller, P., Mallik, N., Feurer, M., Sass, R., Awad, N., Lindauer, M. & Hutter, F., 2021, (E-pub ahead of print) Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track). 36 p.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Published
Hyperparameters in Contextual RL are Highly Situational
Eimer, T., Benjamins, C. & Lindauer, M. T., 2021, International Workshop on Ecological Theory of RL (at NeurIPS).Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- Published
Maschinelles Lernen in der Prozessplanung
Stürenburg, L., Denkena, B., Lindauer, M. & Wichmann, M., 2021, In: VDI-Z Integrierte Produktion. 163, 11-12, p. 26-29 4 p.Research output: Contribution to journal › Article › Research › peer review
- E-pub ahead of print
Prior-guided Bayesian Optimization
Souza, A., Nardi, L., Oliveira, L. B., Olukotun, K., Lindauer, M. & Hutter, F., 2021, (E-pub ahead of print) Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
TempoRL: Learning When to Act
Biedenkapp, A., Rajan, R., Hutter, F. & Lindauer, M., 2021, (E-pub ahead of print) Proceedings of the international conference on machine learning (ICML). 18 p.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- E-pub ahead of print
Well-tuned Simple Nets Excel on Tabular Datasets
Kadra, A., Lindauer, M., Hutter, F. & Grabocka, J., 2021, (E-pub ahead of print) Proceedings of the international conference on Advances in Neural Information Processing Systems (NeurIPS 2021). 23 p.Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
- 2020
- E-pub ahead of print
Squirrel: A Switching Hyperparameter Optimizer
Awad, N., Shala, G., Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Doerr, C., Lindauer, M. & Hutter, F., 16 Dec 2020, (E-pub ahead of print) 3 p.Research output: Working paper/Preprint › Preprint